Self-Supervised Representation Learning:
Learning a representation space without annotations saves substantial time and energy when creating deep learning solutions.
Motivated by this, recent works explore various pretext tasks to generate supervision signals for training deep learning architectures [1]}, [2]}, [3]}, [4]}, [5]}.
A few pretext tasks for learning image representations include image colourization [6]}, [7]}, object counting [8]}, [9]}, solving jigsaw puzzles [10]}, [11]}, predicting image rotations [12]}, [13]}, and reconstructing input images [14]} from noise [15]}.
Pretext tasks for learning video representations include predicting future frames [16]}, [17]}, [18]}, [19]}, [20]}, [21]}, using temporal order and coherence as labels [22]}, [23]}, [24]}, [25]}, [26]}, and predicting the arrow of time [27]}.
